Diagnosis and treatment of a spectrum of musculoskeletal diseases stand to benefit from high-quality, accurate imaging and morphological assessment as provided by peripheral quantitative CT (pQCT). For example, osteoporosis presents a growing health burden involving reduction in bone density leading to fragility fractures. Similarly, and particularly in an aging and obese population, osteoarthritis (OA) is an increasingly common degenerative joint disease caused by biomechanical stressors and an attendant disregulated response characterized by cartilage loss, with concomitant new bone growth, subchondral bony cysts, and other morphologic changes. Rheumatoid arthritis (RA) and other forms of inflammatory arthritis are autoimmune diseases characterized by hypertrophic synovium, cartilage loss, bone erosion, and ten-don damage. Such pathologies across a spectrum of bone and joint disorders exhibit signatures in intra-articular morphology, bone density, and bone morphometry, and the ability to more accurately assess these structures quantitatively could provide a means of earlier detection and improved assessment of treatment response.
Bone mineral density (BMD) is commonly measured for characterization of osteoporosis using dual-energy x-ray absorptiometry (DEXA) or quantitative CT (QCT). Other image-based measures present additional, potentially more sensitive assessments of pathology, including bone volume fraction (BV/TV), trabecular thickness (Tb.Th), structure model index (SMI), degree of anisotropy (DA), and high-resolution characterization of the joint space morphology. Such metrics have been conventionally challenged because of the limited spatial resolution of clinical (whole-body) CT scanners and have therefore been less frequently utilized. However, these potential biomarkers offer important insight into different bone and joint-related disorders, disease progression and response.
For example, osteoporosis is a common metabolic bone disorder that causes bone fragility and consequent fractures. In the US alone, osteoporosis is responsible for about 1.5 million vertebral and non-vertebral (mainly hip and wrist) fractures each year. Early detection and quantitative assessment of osteoporosis and fracture risk, predominantly depends on bone mineral density (BMD) measurements. Dual-Energy X-ray Absorptiometry (DXA) can be used to provide BMD measurements. However it is only able to measure areal densities.
Quantitative Computed Tomography (QCT), on the other hand, can be used to calculate volumetric densities and provide significantly more accurate BMD measurements. Therefore, QCT is becoming a widely accepted method for BMD assessment. In QCT, some form of calibration or reference phantoms are imaged either before or during a patient scan at close proximity to the desired anatomy. The attenuation coefficients and known densities of the phantoms are then used to extrapolate the unknown density of the patients' bony structures. However, variability in phantom location can cause degradation in BMD accuracy, and positioning of the phantoms can affect overall workflow, total scan time, and patient comfort. Also, variability in phantom location requires user interaction in localizing them during BMD calculations and can affect the accuracy of BMD estimates. Traditional CT scanner designs do not directly incorporate any form of calibration phantoms; rather, phantoms are usually employed as an add-on component, typically laid on the scanner bed under the patient or applied in calibration tables generated before (or after) the imaging exam. As a result, densitometry information can only be derived from the CT scan if an additional calibration phantom is introduced at the time of the scan or, perhaps, if a separate scan is required for quantitative imaging purposes.
It would therefore be advantageous to provide a new system and method for integrating the reference calibration system directly into the scanner and gantry enclosure, such that the phantoms are always at a predetermined location within the scanner geometry and field-of-view (FOV). The reference phantoms will therefore be present during each scan, and there is no need for add-on devices or repositioning the phantoms for each scan. Direct integration of the calibration system with the CT scanner is expected to improve the accuracy of BMD measurement and improve clinical workflow.